Identification of novel CRBN molecular glue ternary binders from a highly diverse CRBN MG library

High-throughput screening (HTS) of cereblon (CRBN) molecular glue (MG)-biased libraries remains a key strategy for proof-of-concept studies and hit identification against emerging targets. Because the CRBN binding pocket and its induced interface can adopt multiple conformations, and the substrate binding pocket is often unpredictable at the early stages of discovery, the structural diversity of both CRBN binders and substrate-recognition motifs is crucial to screening efficiency.In this work, we constructed a high-quality, CRBN MG-biased library derived from more than 40 validated CRBN binders. To further enhance chemical diversity, we introduced novel fragments using a tailored AI-based molecular generation model, designed to maximize variations in substrate-binding motifs. HTS case studies demonstrated that this library effectively identified CRBN-substrate ternary binders and degraders with unique scaffolds, representing promising starting points for further optimization.